Core POS Tagging Terms

Part-of-Speech (POS) Tagging: The process of assigning grammatical categories (tags) to each word in a sentence based on its definition and context.

POS Tag: A label that indicates the grammatical category of a word (e.g., NOUN, VERB, ADJECTIVE, ADVERB).

Tagset: A standardized set of POS tags used in linguistic annotation. Examples include Penn Treebank tagset, Universal POS tags.

Lexical Ambiguity: The phenomenon where a single word can have multiple grammatical functions depending on context (e.g., "book" as noun vs. verb).

Context: The surrounding words or linguistic environment that helps determine the correct POS tag for an ambiguous word.

Sequence Labeling: A machine learning task where labels are assigned to each element in a sequence, which POS tagging exemplifies.

Grammatical Categories

Noun (NN): A word representing a person, place, thing, or concept. Examples: cat, house, freedom.

Verb (VB): A word expressing an action, occurrence, or state of being. Examples: run, exist, sleep.

Adjective (JJ): A word describing or modifying a noun. Examples: beautiful, tall, red.

Adverb (RB): A word modifying a verb, adjective, or another adverb. Examples: quickly, very, often.

Pronoun (PRP): A word substituting for a noun. Examples: he, she, it, they.

Determiner (DT): A word specifying which entities are being referred to. Examples: the, a, an, this, these.

Preposition (IN): A word showing relationships between other words. Examples: in, on, at, under.

Conjunction (CC): A word connecting words, phrases, or clauses. Examples: and, but, or, while.

Interjection: A word expressing emotion or sudden feeling. Examples: oh, wow, alas.

Algorithm-Specific Terms

Hidden Markov Model (HMM): A probabilistic model that assigns POS tags by considering transition probabilities between tags and emission probabilities of words given tags.

Conditional Random Field (CRF): A discriminative probabilistic model that can incorporate rich features and dependencies for sequence labeling tasks like POS tagging.

Transition Probability: In HMM, the probability of moving from one POS tag to another in a sequence.

Emission Probability: In HMM, the probability of observing a particular word given a specific POS tag.

Viterbi Algorithm: A dynamic programming algorithm used to find the most likely sequence of POS tags given a sentence.

Feature Function: In CRF, a function that captures relevant properties of the input (words) and output (tags) for making predictions.

Context and Features

Unigram: Using only the current word as a feature for POS tagging, without considering context.

Bigram: Using the current word and one neighboring word/tag as features for POS tagging.

Trigram: Using the current word and two neighboring words/tags as features for POS tagging.

N-gram: A sequence of n consecutive elements (words or tags) used as features in POS tagging models.

Contextual Features: Information about surrounding words, capitalization, word length, prefixes, suffixes used to improve tagging accuracy.

Out-of-Vocabulary (OOV): Words that appear in the test data but were not seen during training, posing challenges for POS taggers.

Evaluation Terms

Accuracy: The percentage of correctly tagged words in a test dataset, the primary evaluation metric for POS tagging.

Precision: For a specific POS tag, the proportion of words tagged with that label that are actually correct.

Recall: For a specific POS tag, the proportion of words that should have that tag that are correctly identified.

F1-Score: The harmonic mean of precision and recall, providing a balanced measure of performance.

Confusion Matrix: A table showing which POS tags are confused with others, helping identify systematic errors.

Cross-Validation: A method of evaluating model performance by training and testing on different portions of the data.

Training and Data Terms

Training Corpus: A collection of sentences with manually annotated POS tags used to train the tagging model.

Test Set: A separate collection of tagged sentences used to evaluate the performance of the trained model.

Annotation: The process of manually assigning POS tags to words in a corpus, typically done by linguistic experts.

Inter-Annotator Agreement: The degree to which different human annotators assign the same POS tags to the same words.

Data Sparsity: The problem that occurs when there isn't enough training data to reliably estimate model parameters.

Smoothing: Techniques used to handle unseen word-tag combinations in statistical models.

Language-Specific Terms

Morphologically Rich Language: A language like Hindi that uses extensive inflection to encode grammatical information.

Agglutinative Language: A language that forms words by combining many morphemes, affecting POS tagging complexity.

Code-Switching: The practice of alternating between languages within a conversation, creating challenges for POS tagging.

Devanagari Script: The writing system used for Hindi and other Indian languages.

Penn Treebank Tagset: A widely used English POS tagset with detailed grammatical distinctions.

Universal Dependencies: A framework for consistent grammatical annotation across languages.

Technical Implementation Terms

Beam Search: An algorithm for finding the best sequence of POS tags by keeping track of the most promising partial solutions.

Forward-Backward Algorithm: An algorithm used in HMM training to compute probabilities efficiently.

Maximum Likelihood Estimation: A method for estimating model parameters by maximizing the likelihood of the observed training data.

Regularization: Techniques to prevent overfitting in machine learning models, important for POS tagging with limited data.

Feature Engineering: The process of selecting and designing input features that help the model make better POS tagging decisions.

Cross-Linguistic Transfer: Using knowledge from one language to improve POS tagging performance in another language.